Imaging and image processing for computer-aided diagnosis and improved patient care are areas of growing interest to dental practitioners. Among areas of particular interest and significance for computer-aided diagnosis, treatment assessment, and surgery is image segmentation, particularly for tooth regions.
Various approaches have been proposed in recent years to tackle the tooth segmentation problem. For example, Shah et al. in a study entitled “Automatic tooth segmentation using active contour without edges”, 2006, IEEE Biometrics Symposium, describe a method for automating postmortem identification of teeth for deceased individuals based on dental characteristics. The method compares the teeth presented in multiple digitized dental records. One step in such a method is the estimation of the contour of each tooth in order to permit efficient feature extraction. It has been found, however, that extracting the contour of the teeth is a very challenging task. In Shah's method, the task of teeth contour estimation is accomplished using the active contour without edges. This technique is based on the intensity of the overall region of the tooth image. For various reasons, the results shown in the Shah et al. publication demonstrate very limited success in tackling this problem.
In an article entitled “Teeth and jaw 3D reconstruction in stomatology”, Proceedings of the International Conference on Medical Information Visualisation—BioMedical Visualisation, pp 23-28, 2007, researchers Krsek et al. describe a method dealing with problems of 3D tissue reconstruction in stomatology. In this process, 3D geometry models of teeth and jaw bones were created based on input CT image data. The input discrete CT data were segmented by a nearly automatic procedure, with manual correction and verification. Creation of segmented tissue 3D geometry models was based on vectorization of input discrete data extended by smoothing and decimation. The actual segmentation operation was mainly based on selecting a threshold of Hounsfield Unit values. However, this method proves not to be sufficiently robust for practical use.
Akhoondali et al. proposed a fast automatic method for the segmentation and visualization of teeth in multi-slice CT-scan data of the patient's head in an article entitled “Rapid Automatic Segmentation and Visualization of Teeth in CT-Scan Data”, Journal of Applied Sciences, pp 2031-2044, 2009. The algorithm uses a sequence of processing steps. In the first part, the mandible and maxilla are separated using maximum intensity projection in the y direction and a step like region separation algorithm. In the second part, the dental region is separated using maximum intensity projection in the z direction, thresholding and cropping. In the third part, the teeth are rapidly segmented using a region growing algorithm based on four thresholds which are used to distinguish between seed points, teeth and non-tooth tissue. In the fourth part, the results are visualized using iso-surface extraction and surface and volume rendering. A semi-automatic method is also proposed for rapid metal artifact removal. However, in practice, it is very difficult to select a total of five different threshold values for a proper segmentation operation. Results obtained from this processing sequence are disappointing and show poor dissection between the teeth.
In an article entitled “Automatic Tooth Region Separation for Dental CT Images”, Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology, pp 897-901, (2008), researchers Gao et al. disclose a method to construct and visualize the individual tooth model from CT image sequences for dental diagnosis and treatment. This method attempts to separate teeth for CT images where the teeth touch each other in some slices. The method is to find the individual region for each tooth and separate two teeth if they touch. The researchers proposed a method based on distinguishing features of the oral cavity structure. The method used initially separates upper and lower tooth regions and then fits the dental arch using fourth order polynomial curves, after a series of morphological operations. This assumes that there exists a plane separating two adjacent teeth in 3D space. In this plane, the integral intensity value is at a minimum. A plane is projected along each arch point and the corresponding integral intensity is computed. The resulting values are then used to draw a profile and, by analyzing all the local minima, a separating point and the position of the separating plane can be determined. The position identification of the tooth region can guide the segmentation of the individual both tooth contours in 2D and tooth surface in 3D space. However, results have shown that Gao's method does not really separate the teeth correctly; as can be seen in the article itself, the separation lines in many cases cut through the teeth.
Various interactive or user assisted segmentation techniques have been developed in the field of medical imaging. These include techniques in which the viewer makes a mark or stroke on a displayed image to help differentiate foreground features from background, as well as eye gaze tracking and other techniques that directly or indirectly obtain instructions from the viewer.
Thus, it is seen that there is a need for a method that provides an improved and more flexible solution for generating foreground and background seeds to assist in teeth segmentation.